624 research outputs found
Reconstructing Human Pose from Inertial Measurements: A Generative Model-based Compressive Sensing Approach
The ability to sense, localize, and estimate the 3D position and orientation
of the human body is critical in virtual reality (VR) and extended reality (XR)
applications. This becomes more important and challenging with the deployment
of VR/XR applications over the next generation of wireless systems such as 5G
and beyond. In this paper, we propose a novel framework that can reconstruct
the 3D human body pose of the user given sparse measurements from Inertial
Measurement Unit (IMU) sensors over a noisy wireless environment. Specifically,
our framework enables reliable transmission of compressed IMU signals through
noisy wireless channels and effective recovery of such signals at the receiver,
e.g., an edge server. This task is very challenging due to the constraints of
transmit power, recovery accuracy, and recovery latency. To address these
challenges, we first develop a deep generative model at the receiver to recover
the data from linear measurements of IMU signals. The linear measurements of
the IMU signals are obtained by a linear projection with a measurement matrix
based on the compressive sensing theory. The key to the success of our
framework lies in the novel design of the measurement matrix at the
transmitter, which can not only satisfy power constraints for the IMU devices
but also obtain a highly accurate recovery for the IMU signals at the receiver.
This can be achieved by extending the set-restricted eigenvalue condition of
the measurement matrix and combining it with an upper bound for the power
transmission constraint. Our framework can achieve robust performance for
recovering 3D human poses from noisy compressed IMU signals. Additionally, our
pre-trained deep generative model achieves signal reconstruction accuracy
comparable to an optimization-based approach, i.e., Lasso, but is an order of
magnitude faster
Fixed-Time Gradient Flows for Solving Constrained Optimization: A Unified Approach
The accelerated method in solving optimization problems has always been an
absorbing topic. Based on the fixed-time (FxT) stability of nonlinear dynamical
systems, we provide a unified approach for designing FxT gradient flows
(FxTGFs). First, a general class of nonlinear functions in designing FxTGFs is
provided. A unified method for designing first-order FxTGFs is shown under
PolyakL jasiewicz inequality assumption, a weaker condition than strong
convexity. When there exist both bounded and vanishing disturbances in the
gradient flow, a specific class of nonsmooth robust FxTGFs with disturbance
rejection is presented. Under the strict convexity assumption, Newton-based
FxTGFs is given and further extended to solve time-varying optimization.
Besides, the proposed FxTGFs are further used for solving equation-constrained
optimization. Moreover, an FxT proximal gradient flow with a wide range of
parameters is provided for solving nonsmooth composite optimization. To show
the effectiveness of various FxTGFs, the static regret analysis for several
typical FxTGFs are also provided in detail. Finally, the proposed FxTGFs are
applied to solve two network problems, i.e., the network consensus problem and
solving a system linear equations, respectively, from the respective of
optimization. Particularly, by choosing component-wisely sign-preserving
functions, these problems can be solved in a distributed way, which extends the
existing results. The accelerated convergence and robustness of the proposed
FxTGFs are validated in several numerical examples stemming from practical
applications
Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction
We propose a general learning based framework for solving nonsmooth and
nonconvex image reconstruction problems. We model the regularization function
as the composition of the norm and a smooth but nonconvex feature
mapping parametrized as a deep convolutional neural network. We develop a
provably convergent descent-type algorithm to solve the nonsmooth nonconvex
minimization problem by leveraging the Nesterov's smoothing technique and the
idea of residual learning, and learn the network parameters such that the
outputs of the algorithm match the references in training data. Our method is
versatile as one can employ various modern network structures into the
regularization, and the resulting network inherits the guaranteed convergence
of the algorithm. We also show that the proposed network is parameter-efficient
and its performance compares favorably to the state-of-the-art methods in a
variety of image reconstruction problems in practice
International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book
The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions.
This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more
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